Modeling metal-sediment interaction processes: Parameter sensitivity assessment and uncertainty analysis. (June 2016)

• Record Type: Journal Article
• Authors: Cho, Eunju; Arhonditsis, George B.; Khim, Jeehyeong; Chung, Sewoong; Heo, Tae-Young
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 159
• Page End: 174
• Publication Date: 2016-06
• Subjects: Sediment-metal modeling -- Parameter uncertainty analysis -- Sensitivity analysis -- Bayesian Monte Carlo -- EFDC -- Geum river; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.026
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Sensitivity and uncertainty analysis of contaminant fate and transport modeling have received considerable attention in the literature. In this study, our objective is to elucidate the uncertainty pertaining to micropollutant modeling in the sediment-water column interface. Our sensitivity analysis suggests that not only partitioning coefficients of metals but also critical stress values for cohesive sediment affect greatly the predictions of suspended sediment and metal concentrations. Bayesian Monte Carlo is used to quantify the propagation of parameter uncertainty through the model and obtain the posterior parameter probabilities. The delineation of periods related to different river flow regimes allowed optimizing the characterization of cohesive sediment parameters and effectively reducing the overall model uncertainty. We conclude by offering prescriptive guidelines about how Bayesian inference techniques can be integrated with contaminant modeling and improve the methodological foundation of uncertainty analysis. Highlights: Sensitivity and uncertainty analysis was performed for sediment-metal modeling. Suspended sediment predictions are sensitive to critical erosion stress. Sediment bed-water partitioning coefficient is critical for metal predictions. River flow dynamics affect contaminant fate and model parameter sensitivity. Strategies to improve uncertainty analysis of sediment-metal modeling are discussed.